April 22 – Weathering The Climate

Today’s factismal: A 90 day weather forecast is useless but a 90 year climate forecast is useful.

If you’ve paid any attention to the fuss over climate change, then you know that one of the common arguments made by non-scientists is that long-term climate forecasts shouldn’t be trusted because we can’t get good weather forecasts more than ten days long. And the non-scientists are right on the last part; any group that claims to be able to forecast the weather more than ten days in advance is simply fooling themselves. But they are wrong on the first part; climate forecasts that extend for a century or more are probably fairly reliable. But why the difference?

An example of weather data that is being collected to help us understand climate (Image courtesy NOAA)

An example of weather data that is being collected to help us understand climate
(Image courtesy NOAA)

The reason that meteorologists can’t predict long-term weather but climatologists can predict long-term climate is because the climatologists cheat. They use a trick that is common in science. It is used in geophysics to help locate oil and gas. It is used in economics to compare GDPs. And it is used in biology to help sequence DNA. The trick is this: by looking at a lot of examples instead of just one, random variations that we call noise average out and the signal is left behind.

A simple example of this is traffic. If I asked you to predict how long it would take you to drive to the grocery store ten miles from your house today, you could probably give me a good estimate. You know what the weather is like and what the traffic is like and if your car needs gas. But if I asked you to predict how long it would take you to drive to the same grocery store two weeks from now, you would probably give me a bad estimate because you wouldn’t know any of the things you need to know to make the prediction. But if I asked you to tell me how long it would take you to drive from California to Maine, you could give me a fairly good estimate no matter when I wanted you to start. That’s because all of those variables average out over the much longer distance so that you can see the underlying pattern.

The daily high and low temperatures at Will Rodgers International Airport

The daily high and low temperatures at Will Rodgers International Airport

Let’s see how that works in practice. We’ll start with the daily maximum and minimum temperatures recorded at one spot. Since I live in Oklahoma City, I’ll use the Will Rodgers International Airport and I’ll pull the data from the National Climatic Data Center (free to the public!). When we plot that data up, we get the chart above. Can you see a pattern? Probably not, because of all of the various things that affect a temperature reading, from cloudiness to el Nino to wind speed.

Converting to monthly averages gets rid of some of the noise

Converting to monthly averages gets rid of some of the noise

So let’s see what happens when we take some of that variation out by looking at the monthly average high and low temperatures. A lot of the daily noise is removed but it is still pretty hard to see if there’s a pattern there or not.

A clear pattern has begun to show up bit there are still some strong local effects

A clear pattern has begun to show up bit there are still some strong local effects

So let’s move the the annual average of the maximum and minimum temperatures. The averaging removes even more of the short-term effects so that a clear long-term pattern is visible. The temperatures, both high and low, tended to be lower in the 1950s than they are now. But what can we do to make things even more obvious?

The global average shows the same trend but much less influence from local factors

The global average shows the same trend but much less influence from local factors

Climatologists use two tricks. First, instead of relying on one spot on the globe, they average values taken from all over. That removes local influences; as a result, the variations are much smaller. That’s why the Will Rodgers curve in the plot above swings from 58°F to 64°F but the global curve only shifts from 56°F to 59°F. (That’s also why a change of just 3.5°F worries so many climatologists; it means much larger changes in many cities.) The second trick is actually the first one – instead of looking at annual averages, they prefer five-year averages.

Spencer's annotated climate chart showing exactly the same results as NASA (Image courtesy Roy Spencer)

Spencer’s annotated climate chart showing exactly the same results as NASA
(Image courtesy Roy Spencer)

And when the data is looked at using those two simple tricks, it becomes clear that the globe is warming thanks to the amount of CO2 that has been added to the atmosphere by man. (Even skeptics like Spencer and Watts agree with this; they just disagree on what happens next.) Our contribution is 100 times greater than that of volcanoes and fifty times greater than that of wildfires; as for the oceans, they absorb ten times more CO2 than they emit, making them net sinks. To put it as simply as possible, teh world is getting warmer and it is our doing.

A comparison of the CO2 emissions from various sources.

A comparison of the CO2 emissions from various sources.

So this Earth Day, why not do something about it? Check the pressure in your tires. If we all drove on properly inflated tires, we’d save 1,250,000,000 gallons of gasoline each year! That means we’d have $2,500,000,000 more to spend on things other than gasoline (like food). Check the insulation in your attic. By doubling the amount of insulation, you could save $112 per year! And check your light bulbs. By switching just one 40 watt bulb to an LED, you could save $4 per year!

You may have noticed the common theme there – doing things to reduce our CO2 emissions is the same as doing things to save us money. For once we can save our planet and make money doing it. So what are you waiting for? Go celebrate Earth Day by saving money!

 

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